Large language models (LLMs) have powerful natural language processing capabilities. In this paper, we systematically review the recent literature in this field and highlight the new research opportunities that LLMs bring to text analysis in economics and finance. First, we introduce GPT and BERT, the two most representative LLMs, as well as a number of LLMs developed specifically for economic and financial applications. Additionally, we also elaborate on the fundamental principles behind applying LLMs for text data analysis. Second, we summarize the applications of LLMs in economic and financial text analysis from two perspectives. On the one hand, we highlight the significant advantages of LLMs in traditional text analysis scenarios, such as calculating text similarity, extracting text vectors for prediction, text data identification and classification, building domain-specific dictionaries, topic modeling and analysis, and text sentiment analysis. On the other hand, LLMs have strong human alignment capabilities, thus opening up entirely new application scenarios, i.e., acting as economic agents that simulate humans in generating beliefs or expectations about texts and making economic decisions. Finally, we summarize the limitations and existing research gaps that LLMs face in pioneering new paradigms of economic and financial text analysis research, and discuss potential new research topics that may arise from these issues.

Enhancing the welfare of the people is one of the core goals of high-quality development in China's new era. Digital financial inclusion plays a crucial role in improving the subjective well-being of Chinese residents. Utilizing the data from the China Household Finance Survey from 2013 to 2019, and integrating city tiers with municipal digital financial inclusion indices, this paper empirically investigates the impact of digital financial inclusion development on residents' subjective well-being using the ordered Probit model. The findings indicate that the development of digital financial inclusion significantly enhances the subjective well-being of residents. In terms of dimensions, its breadth of coverage and depth of use have a positive impact on residents' well-being, while the degree of digitalization has a negative effect. Moreover, the impact of digital financial inclusion development on subjective well-being varies significantly across different relative income and educational levels. Mechanism analysis shows that the development of digital financial inclusion enhances subjective well-being through three pathways: Improving residents' financial literacy, improving economic conditions, and enhancing social security levels.

In order to explore how green technology innovation and the development of the digital economy can jointly promote green economic growth, this paper constructs a general equilibrium model that includes green technology innovation and digital transition. Combining with the real-world data at the city level in China, from the two aspects of economic growth and carbon emissions, it analyzes the impact and the mechanism of action of the digital economy collaborating with green technology innovation on green economic growth. It found that: 1) Green technology innovation has a "U-shaped" impact on economic growth and carbon emissions. That is, after exceeding a specific threshold of technological innovation level, with the continuous increase in the level of green technology innovation, the economic growth rate will continuously increase, and carbon emissions will continue to decrease. Moreover, the development of digital economy will strengthen the impact of green technology innovation, resulting in a steeper "U-shaped" relationship. 2) The development of economic digitalization has both mediating and moderating effects. Green technology innovation has a positive "U-shaped" impact on the development of the digital economy. That is, an increase in green technology innovation can promote the development of digital economy. In turn, the development of digital economy further moderates the impact of green technology innovation on economic growth and carbon emission reduction, strengthening the positive effect of green technology innovation on green economic growth. 3) The digital economy's enhancement of the impact of green technology innovation on green total factor productivity is the primary mechanism by which the digital economy, in collaboration with green technology innovation, drives green economic growth. 4) Policies to promote the development of economic digitalization need to be accompanied by higher carbon taxes. Although there are short-term economic costs, there are advantages in terms of long-term economic growth and environmental quality. This research combines the study of the green transition of economic development with that of digital transition, providing crucial theoretical support for the coordinated advancement of the green and digital transition of the economy to ensure stable economic growth.

This study investigates the impact of two types of FinTech developed and utilized by banks and non-bank financial institutions on fraudulent behavior in China's A-share listed companies. Based on panel data from 2011 to 2020, the research findings suggest that both types of FinTech can suppress corporate fraud by enhancing internal control levels and external monitoring levels. Heterogeneity analysis indicates that the inhibitory effects of both FinTech types are more pronounced in companies with higher levels of digital transformation and lower levels of information disclosure. Additionally, due to differences in operating conditions, strategies, and objectives of FinTech developers, the inhibitory effect of bank FinTech is significant across all firms, whereas the effect of non-bank FinTech is only significant in high-risk firms. When distinguishing types of corporate fraud, both FinTech types significantly inhibit fraudulent activities related to information disclosure, fund utilization, and other categories. Further analysis reveals a complex interaction between the application effects of bank FinTech and non-bank FinTech. Specifically, the inhibitory effect of bank (non-bank) FinTech is significant when the development of other FinTech is high (low). By simultaneously incorporating both types of FinTech and their interaction terms, significant synergistic inhibitory effects are observed in fund misuse and other types of fraud. Finally, the results indicate that the synergistic development of both types of FinTech may introduce potential risks. In summary, this paper, by identifying the impact of FinTech development on corporate fraudulent behaviors, highlights the common characteristics and individual differences of different types of FinTech, emphasizes potential future cooperation opportunities between bank and non-bank FinTech, and points out potential risks in the development of FinTech.

Utilizing the 2015, 2017, and 2019 China Household Finance Survey (CHFS) data, combined with the income transition matrix analysis method and empirical analysis method, this study systematically investigates the impact of digital finance on income mobility and income inequality among rural households. The income transition matrix analysis reveals that rural households using digital finance have a higher probability of upward income mobility compared to those not using digital finance. Empirical research has found that digital finance significantly promotes upward income mobility and significantly reduces income inequality among rural households. The mechanism of action indicates that digital finance enhances households' income mobility by improving financial accessibility, facilitating the accumulation of development factors, and promoting off-farm employment opportunities. Furthermore, compared to middle and high-income rural households, digital finance has a greater impact on financial accessibility, development factor accumulation, and off-farm employment for low-income rural households. This consequently reduces income inequality, showcasing the inclusive growth characteristic of digital finance. Further analysis reveals that digital finance primarily impacts rural households' property income and wage income through these three pathways, ultimately promoting overall income mobility and reducing income inequality among households. Both digital payments and digital wealth management significantly contribute to upward income mobility and the reduction of income inequality among rural households, while digital lending has a negligible impact. This study provides empirical evidence to support the enhancement of policies aimed at fostering sustained income growth for rural households and optimizing the rural income distribution pattern through digital finance.

This paper introduces digital financial capability into the intertemporal decision model, constructs a theoretical analysis framework to explore the impact mechanism of digital financial capability on household wealth accumulation, and conducts an empirical test based on the data of China Household Finance Survey (CHFS). The research shows that digital financial capability can significantly promote household wealth accumulation in China, particularly for rural households and those with low education and low wealth levels. Mechanism analysis shows that increasing financial investment returns and promoting social interaction are two channels through which digital financial capability can improve household wealth accumulation. Further analysis shows that there are structural differences in the impact of digital financial capability on household wealth accumulation, which can improve the allocation of productive assets and financial assets, and reduce the holding of housing assets and other non-financial assets. The above research conclusions provide a new perspective to explain the accumulation of household wealth in China, and also provide a reference for the formulation of relevant policies to promote common prosperity.

Promoting enterprises to accelerate digital transformation is of great significance to enhance the core competitiveness of enterprises, empower the upgrading of traditional industries, generate new forms of business, as well as drive China's digital economy to become better and stronger. From the perspective of enterprises, this paper analyzes the antecedents of enterprises' digital transformation, constructs related indexes based on the text analysis method, proposes a two-factor theoretical model of manager characteristics and dynamic capabilities, and uses the structural equation model based on partial least squares estimation (PLS-SEM). The empirical results show that manager characteristics such as entrepreneurship, digital evangelist and coordinator, as well as corporate dynamic capabilities such as sensing, learning, integrating and coordinating, have a significantly positive role in promoting the tendency and output of digital transformation of enterprises. In addition, manager characteristics can significantly improve the level of enterprises' dynamic capabilities, and the effect of manager characteristics on enterprises' dynamic capabilities and digital transformation is moderated by managers' perception of policy uncertainty. In addition, the above effects are heterogeneous between state-owned and private enterprises, enterprises in the eastern, central and western regions, as well as enterprises in provincial and non-provincial capitals. This paper fills the research gap on the antecedents of digital transformation, and provide a feasible practical path for enterprises to cultivate managers in the digital era and improve their dynamic capabilities.

Promoting mass entrepreneurship and innovation is of great significance for advancing economic structural adjustment, creating new engines of development, enhancing new driving forces for development, and pursuing a path of innovative-driven development, as well as promoting social upward mobility and fairness and justice. This study utilizes data from three rounds of the China Household Finance Survey (CHFS) conducted from 2013 to 2017 to measure the degree of opportunity inequality in China and empirically investigates its effects on resident entrepreneurship and the underlying mechanisms. The results indicate that the rise in opportunity inequality significantly stimulates the entrepreneurial motivation of resident households. For each standard deviation increase in opportunity inequality, the probability of household entrepreneurship increases by 1.14%. Mechanism analysis shows that opportunity inequality stimulates residents' pursuit of status, thereby promoting entrepreneurship among residents. Furthermore, expanded research findings reveal that digital finance strengthens the positive driving effect of opportunity inequality on entrepreneurship. This enhancement effect is only present in urban areas and is achieved by improving loan accessibility. Additionally, this study finds that livelihood-oriented fiscal expenditures also amplify the promotion effect of opportunity inequality on resident household entrepreneurship. In heterogeneous analysis, households with lower educational levels, lower total assets, and no unemployment insurance show stronger entrepreneurial motivations. Finally, this study finds that opportunity inequality suppresses the entrepreneurial performance of entrepreneurial households, indicating that government policies should focus on strengthening equal opportunities and supporting resident entrepreneurship.

In the process of low-carbon transition, enterprises require substantial financial support for related investments. Therefore, the effectiveness of carbon pricing policies depends on a well-functioning financial market. However, in reality, financial markets face various frictions that hinder the flow of capital, leading to inefficient allocation of resources. These frictions may affect corporate investment behavior, thereby weakening the implementation effects of carbon pricing policies. This paper, focusing on the issue of financing constraints, constructs an environmental-dynamic stochastic general equilibrium (E-DSGE) model incorporating a financing collateral constraint mechanism to analyze the impact of financing constraints on the effectiveness of carbon pricing policies and explores corresponding policy responses. The results show that: 1) From the perspective of environmental benefits, financing constraints weaken the "emission reduction effect" of carbon pricing policies, suppress corporate low-carbon investments, and reduce corporate emission intensity; 2) From the perspective of economic costs, financing constraints amplify the cost impact of carbon pricing on enterprises, restrict output growth, and increase the overall economic cost of the low-carbon transition; 3) Introducing carbon asset-backed loans as a complementary measure to carbon pricing policies can effectively mitigate the negative impact of financing constraints on carbon pricing policies; 4) Numerical simulation shows that financing constraints increase the proportion of carbon pricing-related costs in enterprises' total production costs from an average of 15.31% to 19.47% annually, while reducing the annual average scale of low-carbon investments by approximately 37%. Furthermore, providing more carbon asset-backed loans to high-emission enterprises can significantly enhance policy benefits. The conclusions of this paper are of great significance for improving mechanisms for green and low-carbon development and establishing a systematic climate policy framework.

In the context of the development of financial technology, we start with the complex characteristics of financial big data and elaborate on the importance of transfer learning of using multi-source data information to assist target tasks. We explain the significance of transfer learning technology in dealing with data heterogeneity from the perspective of multi-source data, and summarize the relevant concepts and methods of transfer learning technology, including data-driven and model-based transfer learning methods. In addition, this paper proposes the unified framework of transfer learning method based on generalized moment estimation (GMM), gives the effective algorithm of transfer learning, and applies the proposed method to the application of transfer learning in risk value (VaR) and risk measure based on expected quantile (expectile) under multi-source data. Then, we simulate two scenarios where samples are of insufficient or imbalanced sample sizes, respectively, in the application to personal bank credit evaluation, with tests of the prediction accuracy of three transfer learning methods, and analysis of the importance of filtering resource domain information. Finally, we described more application scenarios and development prospects of transfer learning in the financial field.

In recent years, China's food delivery industry has undergone substantial growth, driven by the rapid expansion of the platform economy and the influence of the COVID-19 pandemic. Food delivery services have not only lessened customers' sensitivity to delays associated with in-person dining but have also generated increased market demand for merchants. It is noteworthy that the majority of merchants employ a centralized operational mode, which combines food delivery and dine-in services within a single establishment. However, certain merchants opt for a decentralized approach, wherein they establish dedicated food delivery outlets exclusively handling food delivery orders while maintaining an offline restaurant. To examine the impact of food delivery channels on merchant decision-making, this study establishes a dual-channel service system operating within a congestion-prone environment. It characterizes the equilibrium strategy of customers under the two operational policies and investigates how the quality of food delivery services affects merchant profits. Furthermore, the research reveals the optimal operational approach based on varying levels of delivery quality. The key findings of the study are as follows. 1) In the case of decentralized operations, the service capacity allocated to the food delivery channel by the merchant exhibits a non-monotonic relationship with its quality. This implies that higher food delivery quality may gradually prompt the merchant to shift its focus toward the offline channel. 2) Despite the fact that higher food delivery quality has the potential to attract more customers, the study surprisingly finds that improving food delivery quality may actually reduce merchant profits in both centralized and decentralized scenarios. 3) While decentralized operations may lead to decreased order processing efficiency, adopting this approach can effectively mitigate the cannibalization effect of the food delivery channel and result in higher profits, particularly when food delivery quality is high. Consequently, centralized mode is recommended only when the food delivery quality falls within an intermediate range. Additionally, we further validated the robustness of this conclusion from various perspectives, including marginal costs and delivery fees.

With the rapid development of digital finance, digital transformation has become a strategic imperative for commercial banks. This paper employs a more comprehensive data set from China Banking Database (CBD), and examines the relationship between bank digital transformation and systemic vulnerability risks. Empirical results indicate that there is a significant inverted U-shaped relationship between the level of bank digital transformation and its own systemic vulnerability risks, and the results are robust, remaining valid even after addressing endogeneity issues. Mechanism analyses reveal that bank digital transformation indirectly affects its own systemic vulnerability risks by altering income acquisition efficiency and active risk-taking. The empirical findings of this paper have important practical implications for preventing systemic financial risks in the banking industry and understanding the balance between digital innovation and risk management.

The end-to-end portfolio selection strategy based on deep learning exhibits high decision-making performance, but its black-box nature hinders interpretability of the decision mechanism. In this paper, we propose a comprehensive end-to-end portfolio selection strategy that combines decision-making capability with interpretability using deep learning, reinforcement learning, and knowledge distillation method. Firstly, by leveraging an improved Transformer to alleviate its quadratic complexity issue, a long sequence representations extractor is proposed. Then, through the employment of a cross-assets attention network and reinforcement learning algorithm, a non-linear "black-box" model is constructed to facilitate dynamic allocation in financial assets. Next, by calculating the gradients of model's outputs with respect to the asset features, we compute significance vectors in the feature space to identify key influential features. Finally, a linear regression model is applied to the identified key features, resulting in a straightforward and economically interpretable end-to-end portfolio selection strategy. Empirical results demonstrate that this interpretable end-to-end portfolio selection strategy based on Transformer and key features achieves favorable return and risk performance, with both decision-making power of deep learning and interpretability. This study provides a portfolio selection strategy that combines efficient decision-making capability and interpretability, contributing to the application of deep learning in the financial domain.

Gold and silver, due to their unique financial properties, have become preferred choices for investment and asset preservation. Accurately quantifying and predicting their price fluctuations is crucial for investors' risk management decisions. This paper introduces a rich set of feature variables and employs a forward rolling algorithm to forecast the realized volatility (RV) of gold and silver futures in Shanghai. We compare the performance of various machine learning models under different loss functions and evaluation methods. The results indicate that the gradient boosting decision tree (GBDT) models demonstrate superior performance in forecasting the futures market for precious metals. Furthermore, this study integrates the XGBoost model with interpretability tools to analyze the dynamic contributions of feature variables to the predicted values in the precious metals futures market. It also assesses the heterogeneous impact of significant variables on predictive performance. Our findings reveal the critical role of market sentiment variables, as well as the relative contributions of macroeconomic variables and volatility decomposition variables under different market conditions. The research provides clear evidence for the selection of factors and models in forecasting precious metal futures market volatility, offering credible investment and management recommendations for investors and regulators in this market.

In the context of the new era, the fundamental way to promote high-quality economic and social development is to improve urban development efficiency, and digital economy plays an important driving role in the process. This paper constructs a theoretical analytical framework for digital economy-driven urban development efficiency improvement, and empirically tests the impact of digital economy on urban development efficiency and spatial spillover effects using a spatial and temporal double-fixed spatial Durbin model. This paper finds that: Firstly, digital economy significantly contributes to urban development efficiency in the region and surrounding areas, and the finding still holds through a series of robustness tests. Secondly, digital economy contributes to urban development efficiency by enhancing social, economic and ecological benefits, but the enhancement is limited by the reduction of land benefits, while industrial integration, technological advancement, and urban-rural integration play an important role in its mechanism. Thirdly, the effect of digital economy in driving the improvement of urban development efficiency shows a non-linear trend of "downward and then upward" and spatial spillover characteristics. Fourthly, there is city-level heterogeneity and geographic-area heterogeneity in the impact of the digital economy on urban development efficiency, which means that the role of digital economy in driving urban development efficiency is more pronounced in cities with high administrative levels and large populations, as well as in the eastern and northern regions. The above findings imply that at present, China should take urban development efficiency as an important target to consider for the high-quality economic development, and take the development of digital economy as the main driving force to improve urban development efficiency.

In the numerous terrorist attacks that have occurred worldwide, various terrorist organizations have shown a trend of collaborative cooperation, posing significant challenges to international counter-terrorism efforts. Based on the global terrorism database (GTD), this study constructs a terrorist organization cooperation evolution network from 121,074 terrorist attacks that occurred globally from 2001 to 2018 and conducts a time-series topological structure analysis. Based on the characteristics of terrorist organization cooperation, the network is divided into time slices of three years each to model the flow patterns of terrorist communities at multiple scales. The analysis shows that the robustness of the terrorist organization cooperation network has been continually strengthening over time, which is necessary to develop corresponding strategies to disrupt it. Focusing on the largest connected sub-network within the terrorist cooperation network, whose influence is continuously expanding, this study proposes a community structure-based neighborhood centrality index (CSNC) to measure the importance of nodes in the largest connected component. Experimental results demonstrate that the network disruption strategy based on CSNC, in the process of disintegrating the terrorist cooperation network from 2001 to 2018, achieved a 16.45% maximum reduction in the R value compared to benchmark strategies, proving that the CSNC-based disruption strategy can more effectively dismantle terrorist cooperation networks.

Entrepreneurship is one of the key means to ease the pressure on social employment, and it is also a long-term driving force to ensure medium-high economic growth. The in-depth development of digital inclusive finance has stimulated the vitality of entrepreneurship, but whether it can effectively improve the quality of entrepreneurship is still a topic worth exploring. Using data from the China Household Finance Survey (CHFS), we find that digital inclusive finance has a positive impact on improving the performance of household entrepreneurship. Further analysis shows that optimizing the external entrepreneurial environment such as regional credit environment, regional innovation level, and market integration, is an important way for households to improve their entrepreneurial performance. In addition, based on the differences in the characteristics of entrepreneurial subjects and regional characteristics, the paper finds that the impact of digital inclusive finance on entrepreneurial performance is more significant in groups with medium and high financial literacy, long-distance groups, and groups in more developed areas. This paper has certain reference significance for further promoting the development of digital inclusive finance and better improving the quality of entrepreneurial development.

In recent years, there have been more frequent disasters occurred in China, which pose significant threats to the lives and property of the people. To cope with the increasing complexity and severity of disasters, decision-makers need to store and dispatch emergency supplies rationally based on the real situation. Current studies on regional dispatch considering multiple warehouses and demand points are insufficient, and the problems such as ‘who/how/how much to dispatch’ have not been well-answered. To solve these problems, this paper proposes three regional dispatching strategies (including strict administrative hierarchy supply dispatch, cross-administrative hierarchy supply dispatch and free and nearest supply dispatch strategies) based on a comprehensive summary of relevant case studies, then builds a multi-agent simulation model based on deprivation cost. A simulation experiment is conducted in Mengcheng County, Bozhou City, Anhui Province, and the result shows that when the regional demand is large in a short time, the free proximity strategy can minimize the total social logistics cost. On the contrary, when the regional demand is small, the differences of the total social cost among three strategies are small. In conclusion, our research suggests that, when facing severe disasters and catastrophes, governments should cooperate and coordinate on the dispatching of relief supplies. However, when facing normal disasters without the risk of life, the demand can be satisfied with the strict administrative strategy.

Exploring the impact of uncertainty on the resilience of Chinese residents' consumption is of great practical significance for expanding domestic demand, smoothing the domestic cycle and economic recovery. By constructing a stochastic computable general equilibrium model of China's domestic demand market, this paper measures the impact of uncertainty shocks on the resilience of household consumption from the perspectives of aggregate and structure, and examines the guarantee mechanism of different policy combinations to expand the resilience of household consumption from the institutional perspective. The results show that income level is the most important factor affecting the resilience of residents' consumption, and the impact of multi-risk cross-infection on residents' consumption resistance is greater than the simple superposition of single factors, and the recovery shock may have a reverse impact on different economic indicators. The resilience of urban and rural residents' consumption is structurally different, and the recovery shocks on the supply side and the demand side jointly affect the resilience of the domestic demand market, among which excess supply will also cause a decline in economic benefits. To improve the resilience of household consumption and achieve the goal of expanding domestic demand, it is necessary to integrate the roles of the government and the market, and the policy guidance needs to focus on employment issues and realize the free flow of land and capital factors between regions. This paper further puts forward corresponding policy recommendations. This paper uses the method of calculable general equilibrium to explore the resilience of household consumption, which breaks through the limitations of local equilibrium in existing studies and comprehensively and systematically measures the resilience of household consumption. By introducing random numbers, the impact of uncertainty shocks on the resilience of household consumption is more effectively simulated, which provides more specific and detailed theoretical support and policy enlightenment for the expansion of household consumption.

China's high-quality development and carbon peaking and carbon neutrality goals both require an overall consideration to economic benefits and environmental cost. Transnational investment promotes the reconstruction of global industrial and supply chains, which also leads to dispute of environmental responsibilities under the accounting of economic benefits based on the ownership principle and the accounting of carbon emission based on the territorial principle. In this paper, we employed an inter-country inter-industry input-output database that distinguishes the activities of multinational enterprises (MNEs) and introduced counterfactual analysis and scenario analysis to evaluate impact of structural change in GVC on China's gross national income (GNI) and CO$_2$ emissions. There was significant industrial shift toward China from 2005 to 2016, boosting China's GNI and CO$_2$ emissions by 15.23% and 20.50% respectively compared to 2016 levels. For the future shift, the scenario analysis shows that compared with the relocation of GVC led by developed economies, the relocation led by China would yield lower negative impact on China's GNI when reducing same amount of China's CO$_2$ emissions. The negative impact on GNI and CO$_2$ emissions varies by sector initiating the relocation and by economy undertaking the relocation. Our analysis provides policy implications for China's future GVC relocation and high-quality development.